Agent Loop

Definition

The Agent Loop is the iterative observe-reason-act cycle that an agent executes until a task is completed or a stopping condition is met. In each iteration of the loop, the agent observes the current state and available information, reasons about what action to take next, executes that action via tool calls or direct output, and updates its internal state with the results. This loop continues until the agent determines the task is complete, reaches a maximum iteration limit, hits a confidence threshold that triggers human review, or encounters a terminal error condition.

Engineering Context

The agent loop is the fundamental control structure of autonomous agents. Each iteration: (1) observe the current state and available information, (2) reason about what to do next, (3) act via tool calls or output generation, (4) update state. Loop termination conditions must be explicitly designed; infinite loops are a real failure mode in production. Always set a maximum iteration count as a hard safety bound. In LangGraph, the loop is implemented as a conditional edge from the reasoning node back to itself (when more tool calls are needed) or to a terminal node (when the task is complete). Cost and latency accumulate with each loop iteration, so monitoring loop depth in production is essential.

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